According to the report, while AI may seem "clever," it's really just a set of software tools and math and logic techniques that can solve specific problems. As an example, image recognition technology "is more accurate than most humans," but the same coding can't also address a math problem. As Research Vice President Alexander Linden, one of the authors, explained, "The rule with AI today is that it solves one task exceedingly well, but if the conditions of the task change only a bit, it fails."

Myth 2: AI machines can learn on their own

Currently, human intervention is needed to create an AI system, Gartner stated. Not only are "experienced human data scientists" needed to frame the problem, prepare the data, choose the right datasets and remove possible bias, they also have to "continually" update the software as new data and knowledge come to the forefront.

Myth 3: AI can be made bias-free

Because of the human input needed for AI, it's going to be "intrinsically biased" one way or another, the report asserted. All we can do, Linden said, is "ensure diversity in the teams working with the AI and have team members review each other’s work." These two steps together "can significantly reduce selection and confirmation bias."

Myth 4: AI will only replace the "repetitive jobs," not the ones requiring top degrees

Yes, AI's capabilities in forming more accurate conclusions through "predictions, classifications and clustering" have allowed it to do away with routine tasks. But it can also help in the complex ones too. The report referred to the oft-quoted example of the use of imaging AI in radiology to identify diseases more quickly than highly-trained radiologists. But AI is also surfacing in financial services and insurance for wealth management and fraud detection. "Those capabilities don’t eliminate human involvement in those tasks but will rather have humans deal with unusual cases," the report noted.

Myth 5: Not every company needs to map out its AI future

Gartner said it believes that every company needs to understand how AI will affect its strategy and could be used to address its business problems. "Even if the current strategy is 'no AI’, this should be a conscious decision based on research and consideration," said Linden. And it should be revisited regularly, he added.

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